from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf
from model import MyModel


mnist = tf.keras.datasets.mnist

# download and load data
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# Add a channels dimension
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]

# create data generator
train_ds = tf.data.Dataset.from_tensor_slices(
    (x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)

# create model
model = MyModel()

# define loss
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
# define optimizer
optimizer = tf.keras.optimizers.Adam()

# define train_loss and train_accuracy
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')

# define train_loss and train_accuracy
test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')


# define train function including calculating loss, applying gradient and calculating accuracy
@tf.function
def train_step(images, labels):
    with tf.GradientTape() as tape:
        predictions = model(images)
        loss = loss_object(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

    train_loss(loss)
    train_accuracy(labels, predictions)


# define test function including calculating loss and calculating accuracy
@tf.function
def test_step(images, labels):
    predictions = model(images)
    t_loss = loss_object(labels, predictions)

    test_loss(t_loss)
    test_accuracy(labels, predictions)


EPOCHS = 5

for epoch in range(EPOCHS):
    train_loss.reset_states()        # clear history info
    train_accuracy.reset_states()    # clear history info
    test_loss.reset_states()         # clear history info
    test_accuracy.reset_states()     # clear history info
    
    for images, labels in train_ds:
        train_step(images, labels)

    for test_images, test_labels in test_ds:
        test_step(test_images, test_labels)

    template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
    print(template.format(epoch + 1,
                          train_loss.result(),
                          train_accuracy.result() * 100,
                          test_loss.result(),
                          test_accuracy.result() * 100))
